Tools: OpenStreetMap and QGIS.
Summary: Investigated the accuracy of OpenStreetMap data and mapped public bins in Auckland parks to assess council waste strategy effectiveness.
Findings: Identified patterns of bin distribution related to population density and park use.
Figure 1: Bin locations at Meola Reef Reserve and surrounding
parks.
Tools: Python (Google Colab: GeoPandas, Pandas, Matplotlib), QGIS
Summary: Explored spatial patterns of life satisfaction scores across Auckland, focusing on age demographics and a deprivation index.
Findings: Areas with higher deprivation showed consistently lower life satisfaction.
Table 1: Life Satisfaction Scores Across Ages
Table 2: Deprivation Classifications and Average
Satisfaction Scores
Figure 2: Choropleth map
of life satisfaction across Auckland.
Tools: Google Earth Engine, Sentinel-2 NDVI, LINZ Aerial Imagery, QGIS
Summary: Prioritized areas for riparian restoration using NDVI change analysis and buffer zoning. Temporal change of riparian zone to show progress in the local restoration initiatives. Informing the local communities and directing their efforts.
Findings: Degraded riparian zones identified. Data shared with community groups for restoration planning. Growth in areas which have had focused planting initiatives Investigating priority areas for local restorations projects. To help inform the local communities and direct their efforts.
Figure 3: Temporal NDVI change along the Kapoaiaia Stream (2018,
2021, and 2024).
Figure 4: Priority areas along the Kapoaiaia Stream riparian
zone.
Tools: Google Earth Engine, Sentinel-2 Supervised & Unsupervised Classification
Summary: Mapped and compared lake extent and vegetation development between 2015 and 2024 to evaluate hydrological changes in Egypt’s Toshka Lakes region.
Findings: Detected significant lake expansion linked to regional water management and flooding events. As well as an increase in agricultural sites.
Figure 5: Classification map highlighting water, vegetation, and
ground classes.
Figure 6: Water extent change (2015 vs 2024).
Summary: Provided baseline data for urban expansion monitoring.
Figure 7: Classification of Auckland (2024) with urban, forest,
rural, and water classes.
Tools: Google Colab, Python, Folium
Summary: Assessed accessibility of Seattle’s Airbnb locations for tourists without private vehicles.
Findings: Central districts have high accessibility, while limited accessibility in Peripheral districts.
Figure 8: Percentage of Airbnbs near bus stops by city
area.
Tools: Google Earth Engine, GEOS 16/17, MODIS
Summary: Compared fire detection capabilities of GEOS and MODIS sensors to improve burn area mapping accuracy over Los Angeles.
Findings: GEOS captured fires in near real-time. MODIS provided higher spatial accuracy.
Figure 9: Side-by-side comparison of fire detection maps.
Tools: Google Earth Pro, ArcGIS Pro
Summary: Mapped geomorphic features and landslide prone areas to evaluate terrain features and erosion potential.
Figure 10: Golden Bay Estuary geomorphic map.
Figure 11: Golden Bay catchment geomorphic map.
Used ArcGIS Pro for spatial analysis and data visualization.
Conducted data preparation, spatial joins, and thematic mapping to explore spatial relationships and patterns within datasets.
Created map layouts and visual outputs to support analytical interpretation.
Published processed layers and analysis results directly to ArcGIS Online for web-based use.
Configured interactive web maps using published ArcGIS Pro layers.
Applied custom symbology, filtering, and pop-up configurations to enhance usability.
Used queries and attribute filters to create data-driven insights within AGOL maps.
Designed an interactive dashboard visualizing vehicle crash locations in Dunedin.
Integrated live query tools, charts, and interactive filters for dynamic spatial and statistical exploration.
Connected dashboard components to AGOL data for real-time updates and user interactivity.